Our long term goal is to understand how the nervous system orchestrates its very many degrees of freedom in the service of behavioral function. We view this as a problem or coordination which is a fundamental feature of all living things. We pursue the hypothesis that principles of coordination lie at the level of patterns, defined as stable and reproducible relations among the components of a behavioral or neural system on a chosen level of description. Pattern formation, stability and change are studied both in breadth (different experimental model systems, different kinds of patterns, different levels of description) and depth (especially higher brain functions such as sensorimotor coordination, perception and learning). The research program depends upon a continual interchange between experiment, theory and computation and involves a direct on-site multidisciplinary collaboration between neuroscience, psychology and theoretical physics. The physics combines concepts of self- organization and pattern formation in nonequilibrium systems (collective variables, control parameters, fluctuations, time scales) with the mathematical tools of nonlinear dynamical systems. The psychology concerns the experimental study of how people control voluntary movements, perceive and categorize information, and learn relationships between what they see and what they do. The neuroscience aims at analyzing the spatiotemporally distributed patterns of neural activity generated by the brain when people produce these meaningful behaviors. A strategic focus of the research is around phase transitions or bifurcations where dynamical processes underlying pattern formation, switching, instability and intermittency can be studied in detail and predictions tested. Critical points are crucial because they allow the clear demarcation of patterns enabling the identification of collective variables and their dynamics (equations of motion). Although the same coordination phenomena may be seen at different levels of description, our goal is to find dynamic laws within a given level. The benefits of discovering these (intrinsically nonlinear) dynamic pattern laws are obvious for understanding normal behavior; less obvious is that they also hold the key to understanding abnormal behavior. Where a given system "lives" in the parameter space of its law(s) defines how stable or flexible, normal or aberrant, its behavior may be.